流动电池
电池(电)
计算机科学
荷电状态
卷积神经网络
算法
钒
过程(计算)
电压
锂离子电池
功率(物理)
人工智能
工程类
电气工程
化学
无机化学
物理
操作系统
量子力学
作者
Ran Li,Binyu Xiong,Shaofeng Zhang,Xinan Zhang,Yifeng Li,Herbert Ho Ching Iu,Tyrone Fernando
标识
DOI:10.1016/j.est.2023.106767
摘要
This study proposes an innovative data-driven battery modelling algorithm for vanadium redox flow battery (VRB) in power systems. Unlike the existing battery modelling methods, the proposed algorithm employed the simple but computationally efficient one dimensional convolutional neural network (1D-CNN) technique to learn the nonlinear relationships between VRB current, flow rate, state-of-charge (SOC), and voltage. Compared to the two dimensional CNN, which is widely used in lithium-ion battery modelling and monitoring studies, 1D-CNN eliminates the tedious data re-structuring process and provides better accuracy. Thus, it is more suitable for battery modelling based on one dimensional time series data. Furthermore, 1D-CNN is independent of battery model parameters, allowing it to provide superior modelling performance over the existing electrochemical principle-based and equivalent circuit-based modelling methods that rely on the knowledge of accurate battery model. The validity of the proposed 1D-CNN is verified by experiments.
科研通智能强力驱动
Strongly Powered by AbleSci AI